


import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:



       class        A     :







            '''simple docstring'''







            @staticmethod
            def 					UpperCAmelCase__       (				*_UpperCamelCase    :							Dict			,				**_UpperCamelCase    :							Union[str, Any]):
             pass



@is_pipeline_test
@require_torch
@require_vision
class        A     (		unittest.TestCase	):







     '''simple docstring'''







     lowerCamelCase :						Tuple            =   MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

     def 					UpperCAmelCase__       (				self    :							List[Any]			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Optional[int]			,				_UpperCamelCase    :							Optional[Any]):
      _lowercase:       int						=				pipeline("visual-question-answering"			,				model="hf-internal-testing/tiny-vilt-random-vqa")
      _lowercase:       List[Any]						=				[
          {
              'image': Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
              'question': 'How many cats are there?',
          },
          {
              'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
              'question': 'How many cats are there?',
          },
      ]
      return vqa_pipeline, examples

     def 					UpperCAmelCase__       (				self    :							Optional[Any]			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							List[Any]):
      _lowercase:       Tuple						=				vqa_pipeline(_UpperCamelCase			,				top_k=1)
      self.assertEqual(
          _UpperCamelCase			,				[
              [{"score": ANY(_UpperCamelCase), "answer": ANY(_UpperCamelCase)}],
              [{"score": ANY(_UpperCamelCase), "answer": ANY(_UpperCamelCase)}],
          ]			,				)

     @require_torch
     def 					UpperCAmelCase__       (				self    :							Union[str, Any]):
      _lowercase:       Tuple						=				pipeline("visual-question-answering"			,				model="hf-internal-testing/tiny-vilt-random-vqa")
      _lowercase:       str						=				'./tests/fixtures/tests_samples/COCO/000000039769.png'
      _lowercase:       Optional[int]						=				'How many cats are there?'

      _lowercase:       str						=				vqa_pipeline(image=_UpperCamelCase			,				question="How many cats are there?"			,				top_k=2)
      self.assertEqual(
          _UpperCamelCase			,				[{"score": ANY(_UpperCamelCase), "answer": ANY(_UpperCamelCase)}, {"score": ANY(_UpperCamelCase), "answer": ANY(_UpperCamelCase)}])

      _lowercase:       Tuple						=				vqa_pipeline({"image": image, "question": question}			,				top_k=2)
      self.assertEqual(
          _UpperCamelCase			,				[{"score": ANY(_UpperCamelCase), "answer": ANY(_UpperCamelCase)}, {"score": ANY(_UpperCamelCase), "answer": ANY(_UpperCamelCase)}])

     @slow
     @require_torch
     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      _lowercase:       List[Any]						=				pipeline("visual-question-answering"			,				model="dandelin/vilt-b32-finetuned-vqa")
      _lowercase:       List[Any]						=				'./tests/fixtures/tests_samples/COCO/000000039769.png'
      _lowercase:       List[str]						=				'How many cats are there?'

      _lowercase:       Union[str, Any]						=				vqa_pipeline(image=_UpperCamelCase			,				question=_UpperCamelCase			,				top_k=2)
      self.assertEqual(
          nested_simplify(_UpperCamelCase			,				decimals=4)			,				[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}])

      _lowercase:       Dict						=				vqa_pipeline({"image": image, "question": question}			,				top_k=2)
      self.assertEqual(
          nested_simplify(_UpperCamelCase			,				decimals=4)			,				[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}])

      _lowercase:       List[Any]						=				vqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]			,				top_k=2)
      self.assertEqual(
          nested_simplify(_UpperCamelCase			,				decimals=4)			,				[[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]] * 2			,				)






     @require_tf
     @unittest.skip("Visual question answering not implemented in TF")
     def 					UpperCAmelCase__       (				self    :							Dict):
      pass









"""simple docstring"""


from math import sqrt


def        UpperCAmelCase      (					a__ = 1_00_00_00 ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int

						while num_cuboids <= limit:
												max_cuboid_size += 1
												for sum_shortest_sides in range(2     ,      2 * max_cuboid_size + 1 ):
																		if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
																								num_cuboids += (
																								    min(a__     ,      sum_shortest_sides // 2 )
																								    - max(1     ,      sum_shortest_sides - max_cuboid_size )
																								    + 1
																								)

						return max_cuboid_size


if __name__ == "__main__":
		print(F"""{solution() = }""")